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There is a version of the AI in advertising conversation that is not particularly useful.
It is the version where AI is either the answer to everything — the technology that will make advertising cheaper, faster, more targeted, and more effective across every dimension simultaneously — or the threat to everything — the force that will eliminate creative jobs, homogenise brand communication, and reduce the art of advertising to an algorithmic output.
Both versions are caricatures. Both are easy to hold. And both lead to the same place: either wholesale adoption of AI tools without adequate understanding of their limitations, or wholesale resistance to AI capabilities that are genuinely valuable and already commercially available.
The more useful conversation — the one that actually helps Indian brands make better decisions — is more granular. It asks specifically: which AI capabilities are genuinely valuable and should be embraced now? Which ones come with significant limitations or risks that require scrutiny before adoption? And which ones represent risks to brand integrity, consumer trust, or ethical standards that should be actively refused, regardless of what they can technically accomplish?
This post is that conversation, structured as clearly as we can make it for the Indian market context in 2026.
What Indian Brands Should Embrace
These are AI capabilities with demonstrated commercial value, manageable risks, and clear applicability to the Indian advertising context. Brands that are not using them are leaving genuine efficiency and effectiveness gains on the table.
Programmatic Buying and Real-Time Bidding Optimisation
The AI systems that power programmatic advertising — evaluating thousands of impression opportunities per second, adjusting bids in real time based on performance signals, identifying the specific audience segments and content environments that produce the best campaign outcomes — are demonstrably better at the mechanics of digital media buying than any human buyer operating at equivalent scale.
For Indian brands running digital advertising at any meaningful scale, programmatic buying is not a consideration. It is the reality of how the Indian digital media market functions. The relevant question is not whether to use it but how to configure it correctly — with the right objective, the right audience data, the right brand safety parameters, and the right human oversight of the AI’s optimisation direction.
The embrace here is not uncritical. It is the clear-eyed recognition that the buying decisions programmatic AI makes — at the speed and scale at which they are made — are beyond the practical capacity of human buyers, and that the efficiency and precision improvements this produces are real and commercially significant. Indian brands that are still buying digital media entirely through manual direct deals are operating with a structural disadvantage relative to competitors using well-configured programmatic systems.
AI-Driven Audience Intelligence and Lookalike Modelling
The ability to identify, with AI-driven precision, which consumers are most likely to respond to a brand’s communication — not based on who they demographically resemble but based on how they behaviourally look like the brand’s best existing customers — is one of the most commercially significant AI capabilities available to Indian brand managers right now.
Lookalike modelling, intent modelling based on search and content consumption signals, and predictive conversion modelling trained on first-party CRM data consistently outperform demographic audience targeting in controlled comparisons. The improvement in campaign efficiency — lower cost-per-acquisition, higher conversion rates, better customer quality — is measurable and material.
The embrace of AI audience intelligence should be accompanied by investment in the first-party data infrastructure that makes these models accurate — clean CRM data, proper tracking and identity resolution, consent-based data collection — because the quality of AI audience modelling is entirely dependent on the quality of the data it is trained on.
Creative Testing and Multivariate Optimisation
AI-driven creative testing — running multiple creative variants simultaneously, measuring performance across audience segments and placement types, and algorithmically shifting delivery toward the best-performing combinations — produces learning about what works that would take months of sequential A/B testing to develop through traditional methods.
For Indian brands managing creative across multiple regional markets, multiple languages, and multiple format requirements, this capability has specific value. Dynamic Creative Optimisation systems that assemble personalised creative from component libraries — testing headline variants, image options, call-to-action combinations, and language versions simultaneously — reduce both the cost and the timeline of discovering what resonates with each audience segment.
The embrace here includes an important caveat: AI creative testing optimises for the performance signals it can measure. It will reliably identify which creative variant produces more clicks. It is less reliable at identifying which variant builds stronger brand equity over time. Human creative judgment remains essential for setting the creative parameters within which AI optimisation operates — specifically, ensuring that the variants being tested are all consistent with brand standards and that optimisation toward short-term engagement metrics is not producing creative drift away from brand distinctiveness.
Anomaly Detection and Real-Time Campaign Monitoring
AI monitoring systems that continuously track campaign performance against expected benchmarks — and flag deviations that warrant human review in near real-time rather than waiting for a weekly report — are genuinely valuable for any Indian brand running campaigns with meaningful daily budgets.
A conversion rate drop, a brand safety placement appearing outside configured parameters, a frequency cap malfunction, a creative serving incorrectly — each of these can consume significant budget before a human reviewer conducting weekly performance reviews would catch them. AI monitoring systems catch these issues within hours. For time-sensitive festive campaigns, IPL activations, or product launches where every day of campaign performance matters, this real-time monitoring capability has direct commercial value.
Natural Language Analytics Interfaces
AI-powered analytics tools that allow marketing managers to query campaign and brand performance data in plain language — asking specific questions and receiving specific analytical answers without requiring a formal data request or data science support — genuinely change the speed at which insight informs decision-making in Indian marketing organisations.
For Indian brands where analytical resource is scarce relative to the demand for insight, this democratisation of data access — allowing brand managers to get specific analytical answers at the moment they need them, without waiting for an analyst to run a query — produces meaningfully faster and better-informed decisions. This is available now through most major analytics platforms and is consistently underused relative to its practical value.
Marketing Mix Modelling Enhanced With Machine Learning
For Indian brands spending above ₹15–20 crore annually on media across television, digital, radio, print, and OTT, AI-enhanced Marketing Mix Modelling is the most rigorous and most comprehensive measurement tool available for understanding how each channel is contributing to commercial outcomes.
Unlike digital attribution models that can only measure touchpoints that produce trackable digital interactions, MMM uses statistical analysis across all media spend, sales data, pricing, distribution, and competitive variables to estimate the causal contribution of every channel — including television, radio, and print, which produce no trackable digital signal — to actual revenue. AI enhancement of traditional MMM significantly improves the speed and accuracy of these estimates.
The investment required — in data organisation, in analytical resource, in the 12-to-18-month timeline needed to produce reliable initial results — is substantial. But for brands at the relevant scale, it consistently pays back in allocation improvements within one to two planning cycles.
What Indian Brands Should Question
These are AI capabilities that have genuine potential value but that come with significant limitations, risks, or conditions that require careful scrutiny before adoption. The appropriate stance is neither wholesale embrace nor wholesale rejection — it is informed evaluation against the specific context of each brand’s situation.
AI-Generated Creative Content
AI image generators, copy generators, video generators, and audio generators have reached a level of technical capability where they can produce advertising creative that is plausible, professionally finished, and in some cases genuinely useful for specific applications.
The question Indian brands need to ask of AI-generated creative is not “can it produce something?” but “does what it produces actually serve the brand?” For high-volume, format-driven content — product description variants, search ad copy alternatives, social caption options — AI generation often produces adequate output at a fraction of the cost and time of human production. For brand campaign creative — the communication that is supposed to build the emotional associations and cultural resonance that make a brand worth something — the output of current AI generation systems is consistently adequate-to-competent and rarely genuinely excellent.
The specific India question for AI-generated creative is whether the output reflects genuine cultural authenticity or the surface markers of Indian culture that AI models have learned to reproduce without deeply understanding. AI-generated visuals of Diwali celebrations, AI-generated Hindi copy, AI-generated regional language content — each of these carries the risk of looking right while feeling wrong to the audience it is intended to reach. Native-speaker, native-culture evaluation of AI-generated content for Indian audiences is not optional. It is the minimum standard for responsible deployment.
Automated Budget Allocation and AI Media Planning Tools
AI-powered planning tools that analyse historical performance data and recommend budget allocations across channels — or that dynamically shift budgets between channels based on real-time performance signals — are genuinely useful analytical inputs to media planning decisions. The speed and scale at which they can evaluate allocation scenarios and project outcomes is beyond what human planners can match manually.
The question is whether the recommendations these tools produce are optimising for the right objective, with the right constraints, and with adequate contextual awareness of the factors that the data does not capture.
AI planning tools that are optimising for short-term conversion efficiency will consistently recommend reducing investment in brand-building channels — television, premium OTT, print — whose contribution to long-term brand equity is real but not captured in the performance data the tool is analysing. A brand that delegates budget allocation to an AI tool without explicit human parameters that protect brand-building investment will, over time, find itself with declining brand health and rising performance marketing costs — because the brand equity that makes performance marketing efficient has been optimised away.
Indian market specificity is also a concern. AI planning tools trained primarily on global or Western market data may not adequately reflect the specific dynamics of the Indian media market — the role of regional television, the influence of cricket event cycles on inventory and rates, the tier-2 and tier-3 consumer’s very different media environment. Human planning judgment informed by India-specific market knowledge remains essential alongside any AI planning tool recommendation.
Personalisation at Scale — Including Behavioural Targeting
AI-driven personalisation — serving different messages to different consumers based on their behavioural profiles — is genuinely valuable when the personalisation is relevant and when it is based on signals that the consumer would reasonably expect to inform their advertising experience.
The question is where personalisation becomes surveillance. Indian consumers are increasingly aware of how their data is used to target advertising — and the reaction to advertising that feels unnervingly precise, that seems to know too much about a specific moment or behaviour, is not positive engagement. It is discomfort and distrust.
The line between personalisation that feels helpful — an ad for a product the consumer was genuinely considering — and personalisation that feels invasive — an ad that appears to have been triggered by a private conversation, a health search, or a location visit the consumer did not consciously share — is real and commercially significant. Indian brands should be asking of their personalisation strategy: if consumers knew exactly what data was being used to target this ad and how it was collected, would they feel that the targeting was reasonable? If the honest answer is no, the targeting approach requires revision regardless of its short-term conversion efficiency.
AI-Powered Chatbots and Conversational Marketing
AI-powered chatbots and conversational interfaces for customer engagement — answering product queries, guiding purchase decisions, handling post-purchase issues — have reached a level of capability where they can handle a significant proportion of routine customer interactions competently. The efficiency gains from deploying AI for routine, high-volume customer interactions are real.
The question is whether the deployment of AI in customer interaction is serving or undermining the brand’s relationship with its consumers. A chatbot that resolves a routine query quickly and accurately is a service improvement. A chatbot that is frustrating to use, that cannot handle the query the consumer actually has, that makes it difficult to reach a human, and that leaves the consumer feeling unheard is a brand-damaging experience — regardless of how much it costs less than a human customer service interaction.
For Indian brands where customer relationships are built significantly on warmth and personal connection — where the experience of dealing with a brand is part of the brand’s competitive differentiation — the deployment of AI in customer interaction requires careful design and honest evaluation of whether it is actually improving the customer experience or just reducing the cost of delivering it.
Predictive Models for Creative and Campaign Decisions
AI systems that predict how a campaign or creative will perform before it is launched — pre-testing creative through AI analysis, predicting campaign outcomes through scenario modelling — can compress the timeline and reduce the cost of campaign decision-making when used appropriately.
The question is the degree of confidence to place in these predictions, particularly for brand-building campaigns where the outcome metrics are diffuse and the time horizon for impact is long. AI predictive models perform most reliably in environments with stable historical patterns and abundant training data. They perform less reliably in genuinely novel situations — a new category entry, an unprecedented creative approach, a significant market disruption — where the historical patterns the model was trained on do not adequately predict the outcome.
Indian brands considering AI predictive tools for campaign decisions should ask: what is the historical accuracy of this model’s predictions for campaigns in this category, this market, this format? If the answer is unclear, the predictions should inform rather than determine the decision.
What Indian Brands Should Refuse
These are AI applications in advertising that carry risks significant enough — to brand integrity, consumer trust, ethical standards, or legal compliance — that Indian brands should decline to use them, regardless of what they can technically accomplish or what short-term commercial advantage they might offer.
AI-Generated Fake Reviews and Testimonials
AI systems capable of generating realistic consumer reviews at scale have made fake review programmes operationally trivial in a way that they were not when reviews had to be written individually by humans. The temptation to use this capability to inflate review scores on marketplace listings, app stores, or independent review platforms is understandable in the context of the commercial importance of social proof to Indian consumer purchase decisions.
This is one of the clearest cases of an AI capability that should be refused. Not primarily because ASCI guidelines and e-commerce platform policies prohibit it — though they do — but because the foundation of consumer trust that makes reviews commercially valuable is undermined every time that trust is systematically manufactured. A brand caught generating fake reviews does not just face regulatory consequences. It faces the destruction of the trust that is its most durable competitive asset, in a media environment where the discovery and public exposure of such practices spreads instantly.
The commercial value of genuine reviews, earned through genuine consumer satisfaction, compounds over time. The commercial risk of fake reviews, generated by AI or otherwise, is unbounded and permanent.
Deepfakes and AI-Generated Fabrications of Real People
AI systems that can generate realistic video, audio, or images of real people saying or doing things they never said or did have obvious potential for advertising misuse — fabricating celebrity endorsements, creating realistic-appearing testimonials from real consumers who never gave them, generating fake news coverage of a brand.
This is a refusal that should be absolute and should be codified explicitly in any Indian brand’s AI usage guidelines. Beyond the legal exposure — to defamation, to right of publicity claims, to consumer protection regulations — the reputational damage from the discovery of deepfake advertising is disproportionate and largely unrecoverable. Indian consumers are becoming more sophisticated about identifying fabricated content, and the cultural context of trust in Indian society means that discovery of deliberate fabrication carries consequences that no short-term advertising gain can justify.
This extends to the less obvious applications: AI-generated voice that mimics a specific public figure, AI-generated images that place real people in contexts they never occupied, AI-generated quotes attributed to real people. Each of these is a version of the same refusal.
Manipulative Targeting Based on Psychological Vulnerability
AI systems trained on consumer behavioural data are capable of identifying psychological states — anxiety, loneliness, insecurity, grief, health concern — from digital behaviour patterns, and of timing advertising delivery to maximise response from consumers in those states. A consumer who is exhibiting anxiety signals receives an ad for a financial product. A consumer whose browsing history suggests health concerns receives an ad for an unproven supplement. A consumer whose social media behaviour indicates loneliness receives advertising that exploits rather than addresses that state.
This capability exists. It is not uniformly deployed. And Indian brands should refuse it — not because it is technically impossible or commercially ineffective, but because it is manipulative in a way that violates the basic ethical premise of advertising as a legitimate commercial communication.
The line between persuasion and manipulation is genuinely contested in advertising ethics. But targeting that specifically exploits psychological vulnerability — that identifies consumers when they are at their most susceptible and delivers messages designed to bypass rather than inform rational decision-making — is on the wrong side of that line. Indian brands that refuse this approach are not sacrificing commercial advantage. They are protecting the long-term trust that is their most valuable asset.
Fully Automated Brand Communication Without Human Oversight
AI systems are now technically capable of generating and publishing advertising content autonomously — creating social media posts, updating website content, generating email campaigns, and placing programmatic advertising — without human review of each piece of content before it goes live. The operational efficiency of fully automated brand communication is real and measurable.
The risk is equally real. AI systems do not exercise cultural judgment, ethical reasoning, or brand consistency assessment in the way that human reviewers do. An AI system generating social media content will, at some point, produce something that is contextually inappropriate — offensive to a specific community, insensitive to a current event, inconsistent with the brand’s established positioning — simply because the system is optimising for engagement signals rather than brand integrity.
For Indian brands, where the cultural complexity of communication across regional markets is high and where the consequences of a brand misstep can be severe and rapid in a socially connected consumer environment, fully autonomous brand communication without human review is a risk that no operational efficiency gain justifies. AI as a generation and drafting tool, with human review before publication, is a sensible workflow. AI as a fully autonomous publisher of brand communication is not.
Discriminatory Targeting That Violates Consumer Rights
AI-driven advertising targeting systems are capable of — and have been found to engage in — discriminatory targeting that excludes or includes consumers based on protected characteristics in ways that violate consumer rights and advertising regulations. Advertising for housing, employment, or financial services that excludes consumers based on inferred race, religion, gender, or other protected characteristics is illegal under Indian law and unethical regardless of legality.
Indian brands should explicitly audit their programmatic targeting parameters and lookalike modelling inputs to ensure that these systems are not producing discriminatory outcomes — either through explicit exclusion of protected group identifiers or through proxy variables that have discriminatory impact without explicit discriminatory intent. The fact that a targeting outcome is algorithmically produced does not reduce the brand’s or the advertiser’s responsibility for it.
The Organising Principle: AI as a Tool in Service of Human Judgment
Running through all three categories — embrace, question, refuse — is a consistent organising principle.
AI in advertising is a powerful set of tools. Tools are in service of judgment, not a replacement for it. The efficiency and precision that AI brings to the mechanical dimensions of advertising — the buying, the targeting, the optimisation, the testing — are genuinely valuable when those tools are configured with human strategic intelligence, evaluated against human-defined objectives, and constrained by human ethical standards.
The risks and failures of AI in advertising almost universally originate in the same place: the abdication of human judgment in favour of algorithmic output. The programmatic campaign that optimises toward the wrong objective. The creative testing system that produces brand-inconsistent output because no human is enforcing brand standards on the variant library. The targeting system that produces discriminatory outcomes because no human audited the model for proxy variable bias. The AI content system that publishes inappropriate material because no human reviewed it before it went live.
None of these failures are failures of the AI technology itself. They are failures of the human judgment that should be directing, constraining, and overseeing the AI — and that, in each case, was not doing its job.
For Indian brands navigating the AI in advertising landscape in 2026, the competitive question is not how much of advertising can be automated. It is how well human strategic intelligence and ethical judgment can be combined with AI efficiency and precision to produce brand communication that is simultaneously more effective and more trustworthy than what either alone could produce.
The brands that answer that question well will lead their categories. The brands that answer it poorly — either by refusing AI’s genuine advantages or by delegating judgment to it — will find the competitive landscape increasingly difficult.
Conclusion
AI in advertising is not a single thing to be embraced or refused. It is a set of capabilities with different value profiles, different risk profiles, and different ethical implications — each of which deserves specific and considered evaluation rather than a blanket yes or no.
Indian brands that approach this evaluation with the granularity it requires — embracing what is demonstrably valuable, questioning what comes with significant conditions, and refusing what crosses ethical lines regardless of technical capability — will use AI in ways that build competitive advantage rather than undermining the trust and brand integrity that make their advertising worth anything in the first place.
At Alliance, we have been thinking carefully about where AI genuinely improves what we do for Indian brands — in media buying, in audience intelligence, in campaign optimisation, in analytics — and where it introduces risks that require human judgment to manage. We integrate AI tools where they make our work more precise and more efficient. We maintain human strategic intelligence, cultural understanding, and ethical oversight where these are irreplaceable.
If your brand is trying to work out where AI belongs in your advertising practice — what to invest in, what to scrutinise, and what to actively decline — that is a conversation we are well-positioned to have.